Jose M. Moyano

• PhD in Computer Science
• Associate professor at University of Seville

Background After sustained virological response (SVR), liver stiffness (LS) usually decreases. However, information related to the impact of HIV co-infection in patients with advanced fibrosis is scarce. The aim was to analyze the impact of HIV co-infection on the LS dynamics after HCV cure. Methods Prospective study conducted in the GEHEP-011 mul...

Introduction Liver steatosis (LS) is a condition that is characterised by hepatic fat accumulation unrelated to significant alcohol consumption. This study explored the serum proteomic profile associated with LS in people living with HIV (PLWH). Methods The study cohort comprised 266 PLWH, 21.1% and 78.9% of whom had LS and no LS, respectively. Se...

When data privacy is imposed as a necessity, Federated learning (FL) emerges as a relevant artificial intelligence field for developing machine learning (ML) models in a distributed and decentralized environment. FL allows ML models to be trained on local devices without any need for centralized data transfer, thereby reducing both the exposure of...

Background & objective Nonalcoholic fatty liver disease (NAFLD) is highly prevalent in people living with HIV (PLWH) and the expression of some microRNAs could be useful as biomarkers for the diagnosis of NAFLD. The aim of this study was to identify patterns of differential expression of microRNAs in PLWH and assess their diagnostic value for NALFD...

El mantenimiento de instalaciones industriales ha sido siempre una tarea crítica para garantizar el buen funcionamiento de los sistemas y su disponibilidad. Las estrategias de mantenimiento tradicionales han estado marcadas por enfoques correctivos y preventivos. Sin embargo, los últimos avances en sensorización y aprendizaje automático han impulsa...

The multi-label classification task has been widely used to solve problems where each of the instances may be related not only to one class but to many of them simultaneously. Many of these problems usually comprise a high number of labels in the output space, so learning a predictive model from such datasets may turn into a challenging task since...

Multi-label classification has been used to solve a wide range of problems where each example in the dataset may be related either to one class (as in traditional classification problems) or to several class labels at the same time. Many ensemble-based approaches have been proposed in the literature, aiming to improve the performance of traditional...

BACKGROUND: The dataset from genes used for the prediction of HCV outcome was evaluated in a previous study by means of conventional statistical methodology. OBJECTIVE: The aim of this study was reanalyze this same dataset using the data mining approach in order to find models that improve the classification accuracy of the genes studied. METHO...

Multi-Target Regression problem comprises the prediction of multiple continuous variables given a common set of input features, unlike traditional regression tasks, where just one output target is available. There are two major challenges when addressing this problem, namely the exploration of the inter-target dependencies and the modeling of compl...

BACKGROUND The dataset from genes used for the prediction of HCV outcome was evaluated in a previous study by means of conventional statistical methodology. OBJECTIVE The aim of this study was reanalyze this same dataset using the data mining approach in order to find models that improve the classification accuracy of the genes studied. METHODS W...

Background The dataset from genes used to predict hepatitis C virus outcome was evaluated in a previous study using a conventional statistical methodology. Objective The aim of this study was to reanalyze this same dataset using the data mining approach in order to find models that improve the classification accuracy of the genes studied. Methods...

The multi-label classification task has gained a lot of attention in the last decade thanks to its good application to many real-world problems where each object could be attached to several labels simultaneously. Several approaches based on ensembles for multi-label classification have been proposed in the literature; however, the vast majority ar...

Multi-label classification has attracted increasing attention of the scientific community in recent years, given its ability to solve problems where each of the examples simultaneously belongs to multiple labels. From all the techniques developed to solve multi-label classification problems, Classifier Chains has been demonstrated to be one of the...

In recent years, the multi-label classification task has gained the attention of the scientific community given its ability to solve problems where each of the instances of the dataset may be associated with several class labels at the same time instead of just one. The main problems to deal with in multilabel classification are the imbalance, the...

The great attention given by the scientific community to multi-label learning in recent years has led to the development of a large number of methods, many of them based on ensembles. A comparison of the state-of-the-art in ensembles of multi-label classifiers over a wide set of 20 datasets have been carried out in this paper, evaluating their perf...

The study of problems that involve data examples associated with multiple targets at the same time has gained a lot of attention in the past few years. In this work, a method based on gene expression programming for the multi-target regression problem is proposed. This method solves the symbolic regression problem for multi-target contexts, allowin...

Los Grados Universitarios en sus planes de estudios tienen asignados una serie de créditos optativos, en los que el estudiante tiene libertad para elegir las asignaturas que más le interesen. Esta elección suele ser bastante complicada para los estudiantes, que si bien cuentan con una guía docente de cada asignatura, que les permite conocer los con...

This paper introduces the 3rd major release of the KEEL Software. KEEL is an open source Java frame- work (GPLv3 license) that provides a number of modules to perform a wide variety of data mining tasks. It includes tools to perform data management, design of multiple kind of experiments, statistical analyses, etc. This framework also contains KEEL...

The discovery of fuzzy associations comprises a collection of data mining methods used to extract knowledge from large data sets. Although there is an extensive catalog of specialized algorithms that cover different aspects of the problem, the most recent approaches are not yet packaged in mainstream software environments. This makes it difficult t...

In this article we present an evolutionary algorithm for the optimization of sequences of targets for the multitarget regression algorithm Ensemble of Regressor Chains. This algorithm selects several random sequences or chains of targets where to predict each target, the values of previous targets in the chain are included as features, considering...

The objective of this paper is to present MLDA, a tool for the exploration and analysis of multi-label datasets with both simple and multiple views. MLDA comprises a GUI and a Java API, providing the user with a wide set of charts, metrics, methods for transforming and preprocessing data, as well as comparison of several datasets. The paper introdu...

Multi-label classification with multiple data views is a recent research field not much explored. This more flexible learning approach allows each pattern to be represented by several sets of attributes and each pattern can have simultaneously associated several labels. In this work, an ensemble-based approach, which enables the fusion of views at...